METHOD AND SYSTEM FOR SIMULATING AND OPTIMIZING REVENUE FROM SERVICE FEES IN OR RELATING TO ONLINE SERVICE PROCESSES
A method of managing a billing process is provided to optimize or simulate different models in order to reach a predetermined level of a metric associated with the service. The method includes determining an input model to optimize or simulate, determining existing associated models, comparing the existing model and the input model, iterating one or more parameters of the input model in order to achieve the predetermined level of the metric, and determining the iterated input model.
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The present invention relates to a method and system for checking and optimizing of revenue relating to online service processes, and in particular to system and method associated with a service fee engine.
BACKGROUND OF THE INVENTIONIn the context of the travel industry, users often use travel agents to source and purchase travel products. Traditionally, airlines pay a commission to the travel agent for each ticket sold. However, the advent of low cost carriers has pushed full service carriers to revise this model, and they are now reluctant to pay commissions. This has placed greater pressure on travel agencies, who have to find a new way of being remunerated. To address this issue more and more travel agencies have started to rely on a service fee engine or calculator to assist in computing fees that are paid directly by the customer.
A service fee engine provides an integrated solution for the storage, calculation and collection of service related fees, based on the agent activity and on reservation parameters, such as ticket price or booking class of an airplane etc. With a service fee engine it is possible to determine increased revenues per sale and to charge appropriate service fees when commissions are no longer paid by airlines. The service fee engine can include fee models that are flexible and depend on the requirements of the agent. The service fee engine can interact with other parts of a travel software environment, for example interface records, customer profiles, selling platforms and also global distribution systems (GDS).
US 2005/0004819 (University of Washington) discloses a method and system for providing predictive future costs for an outcome based on historical pricing information in the domain of airline tickets. The method suggests a specific time to buy a ticket in order to optimize profit. There is no suggestion of a solution that overcomes some of the problems currently being encountered by lack of commission and the changing business model for a travel agency.
The present service fee engines do not deal with all the issues currently being encountered by travel agencies in terms of realizing a valid business model. Accordingly, improvements to current systems are required.
One aspect of the present invention is to overcome at least some of the problems associated with the prior art.
A further aspect of the present invention is to provide a way of optimizing agent revenue.
SUMMARY OF THE INVENTIONThe present invention provides a method and system for simulating and optimizing agent revenue, as described in the accompanying claims.
According to one aspect of the present invention there is provided a method of managing a service fee computation process to optimize or simulate different service fee models in order to obtain a predetermined level of a metric associated with the service fee model, wherein the service fee model includes one or more parameters which impact the metric, the method comprising: determining an input service fee model; determining one or more existing associated service fee models; changing the one or more parameters of the input model in an iterative manner to optimise or simulate the or each parameter thereby achieving different values for the predetermined level of the metric; comparing the or each existing model with the input model as the parameters are changed; and selecting a final version of the input service fee model which provides the closest match to the predetermined level of the metric.
According to a second aspect of the present invention there is provided a system for managing a service fee computation process to optimize or simulate different service fee models in order to obtain a predetermined level of a metric associated with the service fee model, wherein the service fee model includes one or more parameters which impact the metric, the system comprising: an input module for inputting an input service fee model; a search module for identifying one or more existing associated service fee models; an optimizer engine for changing the one or more parameters of the input model in an iterative manner to optimise or simulate the or each parameter thereby achieving different values for the predetermined level of the metric; a comparison module for comparing the or each existing model with the input model as the parameters are changed; and a selection module for selecting a final version of the input service fee model which provides the closest match to the predetermined level of the metric.
Reference will now be made, by way of example, to the accompanying drawings.
The present invention is described with reference to the travel environment, although it will be appreciated that it could apply to different environments. For example, any environment where services or goods prices are computed based on several input parameters, such as raw material price etc. The description uses maximizing revenue by optimizing or simulating agent service fee pricing rules as an example. It will be appreciated that other metrics than revenue and other models than agent service fee pricing rules may be equally relevant to the present invention.
Referring initially to
revenue simulation in order to access the impact of new pricing rules on income for the user;
revenue optimization in order to automatically evaluate the optimal or best pricing rules for a specific income target; and
release of pricing rules in order to automate the pricing rule promotion after either simulation or optimization has occurred.
Revenue simulation is a service which enables the user to analyse the use of the services fee engine and pricing rules. The revenue simulator shows customer trends and enables adaptation to these. The revenue simulator is typically structured on four axes as will be described in greater detail below. The first axis involves automatic population of a historical database with key information on the service fee engine activity by the agent revenue optimizer. This allows subsequent realistic replay or running of scenarios to test new or modified pricing rules or other models, for example by running a sample of traffic.
The second axis generates reports containing statistical indicators. In this way, the results from a simulation can be interpreted by the agent revenue optimizer to build different types of reports in order to analyse new modified pricing rules so as to make decisions. An example of a report that can be generated is the service fee use report. This report illustrates the level of use of the service fee engine by showing the amount of computation, the application that initiated the computation and any command used to launch the computation. An alternative report is the activity view. More complex reports of activity may also be offered. In order to generate the activity view a number of different views are used. The views may include sets of parameters which allow a specific part of the travel office activity to be focused upon. The views also choose which statistical indicators are required in the report. Once the view has been created it can be applied to the database to produce a report including all statistical indicators identified in the view either literally or by correspondence to one or more of the parameters. For example, a particularly useful view is the pricing rules use view. This is a view which groups activities by pricing rule elements and which provides a view similar to the service fee use report but for each specific pricing rule.
A third axis identifies non profitable activities in order to improve pricing rules. An agent can identify which activities are profitable and which are not by carrying out a number of different simulations. Pricing rules that are linearly dependent on ticket price could result in a non profitable situation if the most chosen flights are the cheapest. In this situation, a flat fee would be likely to offer a more profitable model. Similarly, pricing rules which depend on the number of operations would be less profitable if travellers did not opt to use changes or special requests in order to increase the number of operations. This third axis also identifies the traveller's main behaviour enabling agents to adapt pricing rules to fit that behaviour and therefore maximise profitability.
A fourth axis simulates the impact of any promotional offers or similar optional inclusions. This allows an agent to determine the impact of a particular promotional offer on its revenue. A simulation with a particular pricing rule corresponding to the promotional offer can be launched. The agent revenue optimizer, in accordance with the present invention, will then provide a report which can be used to compare the impact of special offers on the pricing rule.
The revenue optimization service enables the user to optimize revenue and therefore ensure competitiveness with other agents. Using the revenue optimization service it is possible to choose the required revenue and then adapt the chosen pricing rule to achieve this level of revenue. This is realised by carrying out a linear modification of a set of the parameters of a particular pricing rule in order to achieve the desired revenue with the simulation. As services are not always in linear dependency on the parameters, a method such as the Newton's iteration method may be used to achieve the desired revenue. A graph demonstrating how the Newton's iteration is used to achieve predetermined revenue is shown in
The release rules for pricing will now be described. Once the user or agent has modified or created a pricing rule which is satisfactory in terms of simulation and optimization, it will be a pricing rule that the agent will want to use. As a result of this the agent revenue optimizer automatically releases such pricing rules and introduces them into the pricing rule database making them fully available to use.
The reference to pricing rules is an example of a model (for example a new business model) which may be optimized or simulated by the present invention. It will be appreciated that the present invention could apply to any other appropriate model, for example maximizing sales or reservations, etc.
The implementation of the agent revenue optimizer application feature, in accordance with the present invention will now be described with reference to the mechanism, interfaces and implementation steps. Each of the steps can be provided by a dedicated message or step, one for each type of functionality.
Referring initially to
Referring to
Referring now to
In
Once the defined rule modifications have been made are ready to release, a new version of the pricing rules including these modifications can be released to the user. This is shown in
This invention has been applied to the computation of travel agent service fees in the travel environment. However, it will be appreciated that the invention may apply to other environments, for example other selling and marketing applications such as hotels in a specific zone; combinations of flight and hotel; etc. Also different models can be iterated in order to maximise, optimize or simulate a different metric than profit or revenue. For example maximum uptake or seats may be desirable and the iteration can be used to suggest how this may best be achieved. Any comparisons which relate to pricing rules may also apply to comparing a new model with an old, stored model in other examples where pricing rule is not the model.
It will be appreciated that this invention may be varied in many different way and still remain within the intended scope and spirit of the invention.
Furthermore, a person skilled in the art will understand that some or all the functional entities as well as the processes themselves may be embodied in software, or one or more software-enabled modules and/or devices. In addition any process or method steps may be carried out by an appropriate module, even if that module is not mentioned per se herein.
Claims
1. A method of managing a service fee computation process to optimize or simulate different service fee models in order to obtain a predetermined level of a metric associated with the service fee model, wherein the service fee model includes one or more parameters which impact the metric, the method comprising:
- determining an input service fee model;
- determining one or more existing associated service fee models;
- changing the one or more parameters of the input model in an iterative manner to optimise or simulate the or each parameter thereby achieving different values for the predetermined level of the metric;
- comparing the or each existing model with the input model as the parameters are changed; and
- selecting a final version of the input service fee model which provides the closest match to the predetermined level of the metric.
2. The method of claim 1, further comprising outputting the final version of the input service fee model for use.
3. The method of claim 1, further comprising selecting parameters to change based in the service fee input model.
4. The method of claim 1, further comprising selecting the or each existing service fee model based on the commonality of parameters with the input service fee model.
5. The method of claim 1, further comprising selecting parameters for changing from the list including pricing rules, dates, volumes of product or service for sale, type of product or service, value of product or service.
6. The method of claim 1, wherein the step of changing the or each parameter comprises applying a Newton iteration to obtain an optimized level.
7. The method of claim 1, for managing the service fee computation process for the sale of tickets by an agent.
8. The method of claim 7, further comprising outputting the final version of the input model to the agent for generating service fees for the sales of airline tickets.
9. A computer program comprising instructions for carrying out the method of managing a service fee computation process to optimize or simulate different service fee models in order to obtain a predetermined level of a metric associated with the service fee model, wherein the service fee model includes one or more parameters which impact the metric, the method comprising:
- determining an input service fee model;
- determining one or more existing associated service fee models;
- changing the one or more parameters of the input model in an iterative manner to optimise or simulate the or each parameter thereby achieving different values for the predetermined level of the metric;
- comparing the or each existing model with the input model as the parameters are changed; and
- selecting a final version of the input service fee model which provides the closest match to the predetermined level of the metric;
- when said computer program is executed on a programmable apparatus.
10. A system for managing a service fee computation process to optimize or simulate different service fee models in order to obtain a predetermined level of a metric associated with the service fee model, wherein the service fee model includes one or more parameters which impact the metric, the system comprising:
- an input module for inputting an input service fee model;
- a search module for identifying one or more existing associated service fee models;
- an optimizer engine for changing the one or more parameters of the input model in an iterative manner to optimise or simulate the or each parameter thereby achieving different values for the predetermined level of the metric;
- a comparison module for comparing the or each existing model with the input model as the parameters are changed; and
- a selection module for selecting a final version of the input service fee model which provides the closest match to the predetermined level of the metric.
Type: Application
Filed: Oct 23, 2008
Publication Date: Apr 1, 2010
Applicant: Amadeus S.A.S. (Sophia Antipolis Cedex)
Inventors: Christophe Plat (Antibes), Nicolas Hourdou (Sophia Antipolis Cedex), Mikael Prieur (Antibes)
Application Number: 12/257,002
International Classification: G06Q 30/00 (20060101); G06Q 50/00 (20060101);